NPMixer通过分层邻域块混合，提升多变量时间序列预测精度。
NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
- 分层MLP结构处理非重叠片段，捕捉局部时序动态。
- 在7个基准数据集上28组实验中20次优于现有模型（71.4%）。
- 适合需要高精度时序预测的工业与金融场景。
多变量时间序列预测因局部时序动态与跨变量全局依赖的复杂性仍具挑战。本文提出层次化邻域补丁混合框架NPMixer，其核心为可学习平稳小波变换，能数据自适应地分解信号为趋势与细节成分。引入邻域混合模块，通过一系列分层MLP处理非重叠补丁，学习补丁内及跨补丁的时序模式，扩展感受野以捕获多尺度依赖。对高频成分采用通道混合编码器，学习通道间相关性，同时保持基础趋势稳定。在七个基准数据集上的大量实验表明，NPMixer持续优于最先进模型，在28组评估设置中，20组（71.4%）的均方误差（MSE）表现更优。
原文摘要 · Abstract (English)
Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables. In this paper, we propose \textbf{N}eighboring \textbf{P}atching \textbf{Mixer} (\textbf{NPMixer}), a hierarchical architecture featuring a Learnable Stationary Wavelet Transform that adaptively learns filter coefficients to decompose signals into trend and detail components in a data-dependent manner. Our framework introduces a Neighboring Mixer Block that captures local temporal dynamics through a series of hierarchical MLP layers operating on non-overlapping patches. Specifically, the mixer block utilizes MLPs to learn temporal patterns within and across these patches, expanding the receptive field to capture multi-scale dependencies. A Channel-Mixing Encoder is applied to high-frequency components to learn channel correlations while preserving the stability of the underlying global trend. Extensive experiments on seven benchmark datasets demonstrate that NPMixer consistently outperforms state-of-the-art models, achieving better performance in 20 out of 28 ($71.4\%$) evaluated experimental setups for MSE.
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